At present a large amount of data is accumulated by numerous organizations at all levels. Such data may be stored in a data storage facility or data center employing a large heterogeneous collection of storage devices such as Direct-attached storage (DAS), Network-attached storage (NAS), file servers, storage area network (SAN) attached storage arrays. The ever increasing amount of data imposes a problem in managing the data wherein the storage resources at hand may be utilized in an efficient manner.
Several tools are available for optimal configuration of individual storage devices or optimal configuration of Logical Unit Numbers (LUN) with respect to given device characteristics and workload. Automated Resource Provisioning Tools such as HP Minerva™, and the like for Large-Scale Storage Systems may facilitate automatically and optimally assigning workloads to a set of pre-selected storage arrays and preselected logical units in such arrays given the workload characteristics and array descriptions. However, such tools do not address the problem of consolidating multiple heterogeneous storage devices to a smaller set of storage devices in an optimal way considering a plurality of constraints such as capacity, performance, reliability, operational recovery, disaster recovery, and the like.
The prior art also describes various configuration advisor tools that may facilitate determining an adequate mix of storage devices to be installed for satisfying a given workload at a minimum cost. One example of such a configuration advisor tool is the EDT-CA tool developed at IBM Research and Florida International University. EDT-CA collects I/O characteristics data at level of storage volume extents (i.e. a fixed size portion of a logical volume) and aggregates this data over a plurality of time intervals and calculates the cost optimized mixes of disk types that may be employed by a customer to achieve required performance levels. However, such a solution is concerned with optimal configuration and layout in a single storage system and does not provide a method for overall data center storage consolidation. Moreover, the prior art also fails to describe a solution for data protection and recovery requirements in the target state.
The prior art also describes a plurality of automated planners for storage provisioning and disaster recovery. Such automated planners may comprise a Volume Planner. The Volume Planner takes the space and performance requirements such as I/O demand, read-write ratio, response time, and the like, of the new workload as input and recommends the number and sizes of the new volumes to be created as well as their locations. These are based on careful analysis of the current utilizations of the various subsystem components and their suitability to serve the new workload considering the hierarchical constraints and the space-performance imbalances among the pools and workloads.
Hence, the solutions provided by the prior arts are more concerned with optimal configuration and layout in a single storage system or an optimal assignment of workloads to predefined layouts in a given storage system. Moreover, the prior art does not consider the case wherein multiple workloads running on multiple heterogeneous storage devices/systems in the data center are optimally consolidated to a smaller set of storage devices. Further, the prior art is concerned with Input/output and capacity needs alone and do not address to problems related to data protection and recovery requirements.
In order to solve the above mentioned problems, the present application proposes a system and an underlying method for consolidating a data center containing a plurality of heterogeneous storage devices to a smaller set of refreshed storage systems.
Other features and advantages of the present invention will be explained in the following description of the application having reference to the appended drawings.